جست‌وجوی استاد و پژوهشگر

Angelos Alexopoulosمشاهده پروفایل

استادیار

• Theoretical and Applied Econometrics
• Bayesian Analysis
• Computational Statistics
• +۴ مورد دیگر

Angelos Alexopoulos is an Assistant Professor at the Department of Economics , Athens University of Economics and Business . He has held Research Associate positions at the University of Cambridge, University College London, and University of Exeter in the UK. PhD: Athens University of Economics and Business Research Focus: Computational Statistics, Econometrics, Bayesian Analysis, Network Modelling Publications span Bayesian inference, epidemic forecasting, machine learning for fraud detection, and econometric methodology. Key journals include Journal of the Royal Statistical Society , Journal of Computational and Graphical Statistics , and Statistics and Computing . 2024: Gaussian invariance in MCMC 2024: Epidemic nowcasting models 2023: VAT fraud detection with ML Awards include certifications in Deep Learning (Coursera), Blockchain (edX), and Object-Oriented R Programming (DataCamp).

Gerda Claeskensمشاهده پروفایل

استاد

• Model Selection
• Model Averaging
• Post-Selection Inference
• +۴ مورد دیگر

Gerda Claeskens is a full professor of Statistics at the Faculty of Economics and Business (FEB) at KU Leuven, Belgium. She holds positions in the Operations Research and Statistics Research Group (ORSTAT) and is affiliated with the Leuven Statistics Research Center. Her academic journey includes a Licentiate in Mathematics (Summa Cum Laude) from the University of Antwerp and a Ph.D. in Statistics from Limburgs Universitair Centrum (now Hasselt University). Research Interests: Her work focuses on model selection, post-selection inference, nonparametric methods, and high-dimensional statistics. She has contributed extensively to methodologies like focused information criteria, penalized splines, and quantile regression. Awards and Honors: Notable accolades include Fellow of the American Statistical Association (2019), Goodeve Medal (2017), and Medallion Lecturer (2016). She has also held editorial roles in top journals such as Biometrika and Journal of the American Statistical Association . Teaching and Mentoring: She has advised over 20 PhD students and mentored postdoctoral researchers. Her teaching spans advanced statistical methods, probability theory, and business statistics. She has delivered invited lectures globally, including at the European Meeting of Statisticians and the International Society for NonParametric Statistics. Key Contributions: Her book Model Selection and Model Averaging (2008) is a seminal work in statistical methodology. Her research addresses challenges in high-dimensional data, survival analysis, and model averaging, with applications in finance, insurance, and biostatistics.

• Statistical Learning
• Multivariate Analysis
• Classification
• +۶ مورد دیگر

Yoonkyung Lee is a Professor of Statistics and Computer Science Engineering at The Ohio State University, affiliated with the Department of Statistics in the College of Arts and Sciences. She holds a PhD from the University of Wisconsin-Madison (2002). Her primary research focuses on statistical learning and multivariate analysis, with specializations in classification, kernel methods, and model stability. She has a courtesy appointment in Computer Science and Engineering since 2016 and served as a faculty co-director of the Translational Data Analytics Institute (2020–2022). Her work has been funded by the National Science Foundation (NSF), and she was elected a Fellow of the American Statistical Association in 2015. Her educational background includes a PhD in Statistics from the University of Wisconsin-Madison. Her research interests emphasize developing methodologies for latent structures in multivariate data, computational frameworks for model stability, and predictive modeling. Notable contributions include advancements in kernel discriminant analysis, Bayesian restricted likelihood methods, and sparse logistic tensor decomposition. Prof. Lee serves on editorial boards for journals such as Chemometrics and Intelligent Laboratory Systems , Econometrics and Statistics , and Journal of Machine Learning Research . Her articles span topics like support vector machines, quantile regression, and nonlinear embeddings, reflecting her expertise in bridging statistics and machine learning. Beyond research, she has advised numerous projects and contributed to interdisciplinary initiatives in translational data analytics.

Joseph Antonelliمشاهده پروفایل

استادیار

• Causal Inference
• High-Dimensional Modeling
• Bayesian Methods
• +۳ مورد دیگر

Joseph Antonelli is an Assistant Professor of Statistics at the University of Florida , where he has been since 2018. He holds a PhD in Biostatistics from Harvard University (2015) and completed postdoctoral training there until 2018. His research focuses on causal inference, high-dimensional modeling, Bayesian methods, spatial statistics, and applications in environmental health and criminology. Education: B.S. in Statistics, University of Florida (2011) M.S. in Biostatistics, Harvard University (2013) Ph.D. in Biostatistics, Harvard University (2015) Research Interests: Professor Antonelli develops statistical methods to address complex causal questions in health and social sciences. His work emphasizes robust approaches to confounding adjustment, spatial and environmental data analysis, and policy evaluation. He applies these methods to study air pollution effects, policing policies, and opioid policy impacts, bridging theoretical advancements with real-world applications. Key Contributions: His articles span causal inference techniques, Bayesian modeling, and spatial analysis, with recent work addressing racial bias in policing and air pollution mixtures. His research has been recognized with awards from the Health Effects Institute, JSM Biometrics, and ISBA. Awards: 2020 Health Effects Institute Young Investigator Award 2020 Journal of Speech, Language, and Hearing Research Editors Award 2014 ENAR Distinguished Student Paper Award Advising and Grants: He advises over 15 graduate students and has led grants totaling $3.5M, including NIH funding for auditory deficits in children and CDC studies on firearm policy effects. He also serves as Associate Editor for Bayesian Analysis .

Zheng Fangمشاهده پروفایل

دانشیار

• Econometrics
• Statistical Inference
• Quantile Regression
• +۳ مورد دیگر

Zheng Fang is an Associate Professor in the Department of Economics at Emory University. His research focuses on econometrics, with expertise in statistical inference, nonparametric methods, and hypothesis testing. He holds a Ph.D. in Economics from the University of California, San Diego (2015), an MS in Statistics from the same institution (2013), and a BA and MA in Economics from Tsinghua University (2008–2010). His work emphasizes methodological advancements in econometrics, including quantile regression, shape-restricted inference, and large-scale linear system analysis. Recent contributions address topics like matrix rank testing and convex cone frameworks for hypothesis testing. Fang's research bridges theoretical developments with practical applications, often implemented in statistical software (e.g., Stata). His articles cover diverse subfields, including nonparametric estimation, bootstrapping, and concavity testing, reflecting his commitment to advancing statistical tools for economic analysis. No specific awards or grants are listed, but his CV highlights ongoing contributions to econometric theory and methodology.

• Mathematical Statistics
• Machine Learning
• Kernel Methods
• +۳ مورد دیگر

Prof. Dr. Nicole Mücke is a Professor in the Institute for Mathematical Stochastics at the Carl-Friedrich-Gauss Faculty of Technische Universität Braunschweig. Her research focuses on mathematical statistics, machine learning, kernel methods, and statistical inference. She explores topics such as neural network theory, inverse problems, optimization, and regularization techniques. Her work bridges theoretical foundations with practical applications in areas like distributed computing and uncertainty quantification. Prof. Mücke’s research portfolio includes contributions to empirical risk minimization, neural operator learning, and gradient-based optimization. She investigates the interplay between overparameterization and generalization in machine learning models, as well as the design of efficient algorithms for large-scale problems. Her publications span topics ranging from distributed stochastic gradient descent to localized kernel regression techniques. Her recent work emphasizes theoretical guarantees for learning algorithms, including convergence rates, statistical performance in high-dimensional settings, and the role of regularization in inverse problems. She also explores methodological advancements in spectral methods, algorithm unfolding, and data-splitting strategies to enhance statistical efficiency. Prof. Mücke’s research is characterized by a strong emphasis on rigorously analyzing machine learning algorithms through the lens of statistical theory and functional analysis. Her contributions address challenges in both classical and modern machine learning paradigms, with a focus on bridging the gap between abstract mathematical frameworks and practical implementation.

Abolfazl Safikhaniمشاهده پروفایل

استادیار

• Network Modeling
• High-Dimensional Statistics
• Spatiotemporal Models
• +۵ مورد دیگر

Abolfazl Safikhani is an Assistant Professor in the Department of Statistics at George Mason University. He holds a PhD in Statistics and Probability from Michigan State University and has held prior positions at Columbia University and the University of Florida. His research focuses on network modeling, high-dimensional statistics, spatiotemporal models, and applications in urban planning, neuroscience, and smart cities. He is an Associate Editor for Technometrics , Statistica Sinica , and Data Science in Science . He has contributed to advancements in statistical methodologies for time series analysis, including change point detection, transfer learning, and spatiotemporal modeling. His work bridges theoretical statistics with practical applications in urban growth prediction, healthcare (e.g., cancer drug response modeling), and transportation systems. Recent research trends include leveraging explainable AI for land use modeling, longitudinal omics data analysis, and structural break detection in high-dimensional systems. His publications span theoretical developments and real-world case studies, such as subway ridership during the pandemic and New York City’s taxi demand dynamics. Despite no listed awards, his editorial roles highlight his influence in statistical science. He actively mentors students and collaborates on interdisciplinary projects, emphasizing data-driven solutions for complex societal challenges.

Jaihee Choiمشاهده پروفایل

استادیار

• Statistical Genetics
• Survival Analysis
• High-dimensional Data
• +۳ مورد دیگر

Dr. Jaihee Choi is an Assistant Professor in the Department of Mathematical and Statistical Sciences at Marquette University, located in Cudahy Hall. Her research focuses on statistical genetics, survival analysis, and high-dimensional data applications in biomedical contexts. She teaches courses in statistical methods and has published extensively in peer-reviewed journals such as Statistics in Medicine and JCO Precision Oncology . Her work addresses critical challenges in genetic association studies for complex diseases, including interval-censored outcomes and competing risks frameworks. Recent studies explore germline risk variants in pancreatic cancer and biomarker prediction in breast cancer trials. Dr. Choi collaborates on interdisciplinary projects involving clinical oncology and genetic epidemiology. Professional activities include contributions to statistical methodologies for biomedical data and participation in collaborative research initiatives. Contact: jaihee.choi@marquette.edu or Cudahy Hall 369.

Luella Fuمشاهده پروفایل

دانشیار

• Statistics
• Large-Scale Inference
• Multiple Testing
• +۳ مورد دیگر

Luella Fu is an Associate Professor in the Department of Mathematics at San Francisco State University, part of the College of Science & Engineering. Her research focuses on large-scale statistical inference, with expertise in multiple testing, empirical Bayes methods, and heteroscedasticity analysis. She holds a position within the university's Mathematics department and contributes to advancing methodologies in statistical theory and applications. Her work emphasizes developing robust frameworks for handling heterogeneous data and controlling false discoveries in high-dimensional settings. Key research themes include nonparametric estimation, adaptive model selection, and the impact of standardization in hypothesis testing. She has also explored applications of statistical methods in social network analysis, such as studying smoking behavior through Facebook data. Dr. Fu is affiliated with the Department of Mathematics, located in Thornton Hall 937. She can be reached at luella@sfsu.edu, with office hours available via the department's live schedule.

Edsel Peñaمشاهده پروفایل

استاد

• Mathematical Statistics
• Survival Analysis
• Reliability
• +۵ مورد دیگر

Edsel Peña is a Professor and Chair of the Department of Statistics at the University of South Carolina, within the McCausland College of Arts and Sciences. He holds a BS (Magna Cum Laude) from the University of the Philippines Los Baños and a PhD from Florida State University. His research focuses on Mathematical Statistics, Survival Analysis, Reliability, and Biostatistics. Peña has held roles including visiting professorships at the University of Michigan and served as Executive Secretary of the Institute of Mathematical Statistics (2017–2023) and NSF Program Director (2020–2023). Education: Bachelor of Science in Statistics, University of the Philippines Los Baños (1979) PhD in Statistics, Florida State University (1986) Research Interests: Peña’s work spans foundational statistical theory, survival analysis applications, and high-dimensional inference. He emphasizes methodological development in reliability systems and nonparametric approaches for recurrent events. His recent work includes optimal correlation-based prediction and asymptotic theory for recurrent event models. Awards: Fellow, American Statistical Association Member, International Statistical Institute Fellow, Institute of Mathematical Statistics Michael Mungo Graduate Teaching Award (University of South Carolina) Severino and Paz Koh Lectureship Award Grants & Leadership: Secured funding from NSF, NIH, and EPA. Served on editorial boards of top journals including the Journal of the American Statistical Association and Scandinavian Journal of Statistics. Currently chairs the Department of Statistics at USC.

Sévérien Nkurunzizaمشاهده پروفایل

استاد

• Statistical Inference
• Statistical Modeling
• Change-Point Analysis
• +۵ مورد دیگر

Sévérien Nkurunziza is a Professor in the Department of Mathematics and Statistics at the University of Windsor, within the Faculty of Science. His research expertise lies in statistical inference, stochastic modeling, change-point analysis, and applied probability. He holds a Ph.D. from UQAM, Canada. His research focuses on statistical methodologies including change-point detection, shrinkage estimation, tensor regression, and inference in diffusion processes. He has supervised over 10 undergraduate students and 30 graduate students, contributing to topics such as neuroimaging modeling, high-dimensional data analysis, and mean-reverting processes. Recent work includes advancements in generalized CIR models, improved matrix estimators in high-dimensional settings, and inference methods in time-varying diffusion processes. His contributions span theoretical developments and applied statistical challenges. Advising and supervision highlights include mentoring current PhD students (Sathish Pichika, Ran Sun) and M.Sc. students (Dilay Gumus, Lavanya Perera Dharmasena). Past students have contributed to tensor regression, change-point modeling, and diffusion processes. His research is published in journals like Annals of Applied Probability, Journal of Multivariate Analysis, and Statistical Inference for Stochastic Processes.

Anindya Royمشاهده پروفایل

استاد

• Time Series Analysis
• Nonparametric Bayesian Analysis
• High-dimensional Estimation and Inference
• +۳ مورد دیگر

Anindya Roy is a Professor of Statistics in the Department of Mathematics and Statistics at the University of Maryland, Baltimore County (UMBC). His research focuses on time series analysis, Bayesian methods, high-dimensional statistics, and applications in biomedical and engineering domains. He serves as an Area Editor for Statistical Analysis and Data Mining , and on editorial boards for Sankhya, Ser. A and Statistics in Medicine . His research interests include nonparametric Bayesian approaches, geometric constraints in statistical models, and sequential experimental design. He has received research funding from the National Science Foundation (NSF DMS–2210282). Recent work addresses privacy-preserving data synthesis, causal inference in time series networks, and fetal growth modeling. Key contributions include methodologies for high-dimensional time series with directed acyclic graphs, Bayesian approaches for relational graph estimation, and statistical techniques for medical applications such as cyclotron ion source development and cervical cancer screening risk prediction. Notable collaborative efforts involve proteomic analysis of chemical mixtures, oncology trial design, and biomarker studies. His work bridges theoretical statistics with applied problems in health sciences and engineering, emphasizing interdisciplinary impact.

Asokan Variyathمشاهده پروفایل

استاد

• Statistical Process Control
• Experimental Design
• Machine Learning
• +۲ مورد دیگر

Asokan Variyath is a Professor of Statistics at Memorial University of Newfoundland, specializing in experimental design, statistical process control, and educational statistics. Education: Ph.D. in Statistics, University of Waterloo (2006) S.D.P Fellow (SQC & OR), Indian Statistical Institute (1991) Post Graduate Diploma in SQC & OR, Indian Statistical Institute (1988) M.Sc. Agricultural Statistics, Kerala Agricultural University (1987) Research Interests: Develops novel statistical methods in experimental design, quality control, and multivariate analysis. Applied research spans fisheries management, reliability engineering, and computational statistics. Created educational statistical applets to enhance student learning. Publication Trends: Recent publications focus on Bayesian methods, statistical education tools, and applications in fluid dynamics. Methodological work emphasizes variable selection, longitudinal data analysis, and nonparametric techniques. Leadership: Director of Statistical Consultancy Centre since 2010. Former Chair of Master of Data Science program and Deputy Head for Graduate Studies. Received Dean's Service Awards in 2023 and 2024.

Magda Peligradمشاهده پروفایل

استاد

• Probability Theory
• Stochastic Processes
• Limit Theorems
• +۲ مورد دیگر

Magda Peligrad is the Charles Phelps Taft Professor in the Department of Mathematical Sciences at the University of Cincinnati. She specializes in probability theory and stochastic processes, particularly limit theorems for dependent structures. Her research has been continuously supported by NSF and NSA grants since 1996. Education: Ph.D. from Center of Statistics of the Roumanian Academy of Science (1981) Her research focuses on modeling dependence structures through martingale approximation techniques and spectral analysis of stochastic processes. This work bridges probability theory, statistics, and ergodic theory, with applications to random matrix theory and Gaussian approximations. Her publications demonstrate consistent focus on limit theorems, Markov processes, and dependence structures across random fields. Recent work emphasizes non-stationary systems and refined convergence rates for complex stochastic systems. Major Awards: Fellow of Institute of Mathematical Statistics (1995) Distinguished Research Taft Professor (2003) University of Cincinnati Graduate Fellow (2017) As primary advisor, she has guided 8 PhD students and hosted 2 postdoctoral researchers. Current grant support includes NSF funding for 'Asymptotic results for stochastic processes via new projective methods' (2021-2024).

Erik Van Vleckمشاهده پروفایل

استاد

• Applied Mathematics
• Numerical Analysis
• Numerical Methods for PDEs
• +۳ مورد دیگر

Erik Van Vleck is a Professor of Mathematics at the University of Kansas, affiliated with the College of Liberal Arts & Sciences. His research focuses on applied mathematics, numerical analysis, and numerical methods for partial differential equations (PDEs), with particular emphasis on data assimilation and reaction-diffusion systems. His work bridges theoretical advancements and practical applications in computational dynamics, environmental modeling, and ecological systems. Van Vleck’s research interests include multi-scale data assimilation techniques, adaptive mesh methods, and the numerical analysis of traveling wave solutions in discrete and continuous systems. He has contributed to understanding pattern formation in ecological models, particularly in savanna ecosystems and fire-driven dynamics. His methodological innovations span numerical methods for PDEs, reduced-order modeling, and stochastic filtering approaches for complex systems. His recent work highlights trends in developing robust adaptive frameworks for data assimilation, integrating advanced numerical techniques with real-world applications in geophysics and environmental science. This includes projects on metric tensor-based mesh adaptation and projected data assimilation using sliding window methods. His contributions to theoretical foundations include spectral stability analyses for discrete dynamical systems and bifurcation studies in reaction-diffusion equations. Van Vleck has collaborated extensively across disciplines, applying mathematical tools to ecological, climatological, and biomedical systems. His research often addresses computational challenges in high-dimensional spaces, emphasizing efficiency and accuracy in modeling complex phenomena. Despite the breadth of his contributions, no specific grants or advising roles are detailed in the provided materials.